Technical deep dives on context engineering, semantic models, and building RAG systems that survive production — written by the team behind TheHaze.

Do life sciences teams need a graph for semantic AI , or is hybrid retrieval enough? A tier-based decision framework for enterprise context layers.
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How to design retrieval that works in regulated life sciences : ingestion, hybrid indexing, ranking, and governed context packaging.
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The bottleneck isn't the model: it's what never reaches it. How to design a retrieval layer that sends only the signal your LLM needs , especially for unstructured, multi-modal corpora.
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DIY. A practical framework for evaluating embedding models and ranking pipelines on your own data , not someone else's leaderboard.
Read article →Extended PDF guide : how to build evaluation sets, measure Recall@k and MRR on your corpus, and avoid leaderboard-driven mistakes when choosing embedding and ranking stacks.
Tier-based decision matrix for life sciences context architecture ; when hybrid retrieval is enough, and when governed mappings or a graph are required.